Certified: The GIAC GSOM Audio Course

In this episode, we’re going to talk about what happens after a monitoring program goes live and the alert queue starts filling up faster than humans can reasonably keep up. At first, many new learners assume that noise is just the price you pay for visibility, as if a busy dashboard proves the S O C is working. The hard truth is that persistent noise is not a sign of security maturity, because it drains attention, increases mistakes, and hides the rare events that truly matter. The good news is that noisy detections are not a permanent condition, because they can be tuned, and tuning becomes far more effective when it is driven by feedback loops rather than by guesses. A feedback loop is the practical habit of using what you learn from real alert outcomes to adjust detections so that the same low-value work does not keep repeating forever.

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A useful starting point is to define what we mean by noisy detections, because noise is not just the number of alerts, and it is not always the same as false positives. Noise is any detection output that consumes analyst time without producing proportional security value, which can include alerts that are technically correct but operationally unhelpful. For example, an alert might correctly notice that a system changed, yet the change is routine and expected, and the alert adds little beyond what operations already knows. Another alert might catch a real misconfiguration, but it fires so often and with such vague context that analysts cannot separate urgent cases from minor ones quickly. Noise also includes duplicate alerts, where the same underlying behavior generates many separate notifications, creating the illusion of many problems when there is really one. The danger is that noise does not just waste time, because it shapes behavior, teaching analysts to ignore certain alert types or to close them quickly without proper validation. Once that habit forms, true positives can slip through, which is why tuning is both a quality issue and a risk issue.

As the alert queue grows, backlog becomes the visible symptom, but the deeper problem is that backlog changes decision-making. When analysts face more work than time, they start triaging based on fatigue instead of evidence, and the organization shifts from thoughtful response to survival mode. Backlog also breaks the time value of many detections, because an alert about suspicious activity loses much of its usefulness if it is reviewed days later. The result is a cycle where delayed triage creates less certainty, less certainty creates more conservative escalation, and more escalation creates more work, which grows backlog further. This is why tuning must focus on shrinking the backlog over time, not just on making charts look better. Shrinking backlog means reducing avoidable work, improving clarity so decisions are faster, and concentrating attention on the alerts that have the best chance of preventing harm. When you think about tuning through the lens of backlog, you stop treating it as a technical hobby and start treating it as operational recovery.

The most reliable way to tune is to treat each alert as a hypothesis that was tested, and the outcome of that test becomes your feedback signal. If the alert was a true positive, you ask what made it detectable and what information made it confirmable. If the alert was a false positive, you ask why the detection believed it was suspicious and what context would have clarified that it was normal. If the alert was inconclusive, you ask what evidence was missing and whether the detection should be changed or whether collection and enrichment need improvement. This approach keeps tuning grounded in reality because it uses actual outcomes rather than imagined scenarios. It also prevents a common mistake where teams tune only based on annoyance, suppressing alerts because they are frequent without understanding whether they sometimes capture real issues. When you link tuning to outcomes, you can prioritize changes that reduce repeated low-value work while protecting the detections that carry real signal. Over time, the feedback loop becomes a learning engine, and the alert system starts improving with every handled case.

To make feedback loops practical, you need a consistent way to capture the reasons alerts were closed, because tuning depends on understanding patterns rather than remembering individual frustrations. When closures are vague, like benign or not an issue, you lose the details that explain what normal behavior looked like and why the alert fired anyway. When closures capture specific reasons, like expected maintenance, scheduled job, known business process, or misclassified asset criticality, those reasons become tuning targets. This is also where consistent classification matters, because you want to compare outcomes within the same alert category, not mix unrelated patterns together. A detection that fires during routine account provisioning might need different tuning than a detection that fires during nightly backups, even if both look like unusual access. Capturing closure reasons does not need to be heavy, but it must be consistent enough that you can see trends, such as one alert type producing a high volume of predictable false positives. That trend is what tells you where tuning effort will deliver the greatest backlog reduction.

Once you can see patterns, one of the highest-return tuning strategies is to improve specificity by adding context conditions that separate normal from abnormal. Many noisy detections are noisy because they treat all identities, assets, and times as equal, which is rarely true in a real organization. A login pattern might be normal for a system administrator but abnormal for a finance role, so adding role context can reduce false positives without reducing security coverage. A change event might be normal in a development environment but risky in production, so adding environment context can tighten focus. A connection pattern might be expected during an update window but suspicious outside that window, so adding time context can make the detection behave more intelligently. This is why tuning often depends on enrichment, because enrichment provides the context fields that make these distinctions possible. The key is to add context carefully so the detection becomes more meaningful, not so it becomes overly narrow and blind. Good tuning uses context to make the alert align with the use case rather than making it disappear.

Another powerful tuning approach is to redesign detections around behavior sequences instead of single events, because single-event alerts are often noisy by nature. Many legitimate activities produce events that look suspicious in isolation, such as a failed login, a permission change, or a new network connection. When you combine signals into a sequence, you raise confidence because the pattern becomes less likely to occur accidentally. For example, repeated failures followed by a successful login is more meaningful than failures alone, and a privilege change followed shortly by new sensitive access is more meaningful than a privilege change alone. Sequence-based detections also support faster triage because the alert itself tells a coherent story rather than forcing the analyst to build the story manually. This does not mean every detection must be complex, because complexity can be fragile, but it does mean that high-noise single events are often better used as supporting evidence rather than as primary triggers. When tuning reduces single-event triggers and promotes behavior patterns, the alert queue often shrinks dramatically without losing meaningful visibility.

Noise can also come from duplication, and reducing duplication is one of the easiest ways to shrink backlog because it removes repeated work without changing detection logic. Duplicate alerts happen when one underlying activity triggers multiple rules, or when one rule triggers repeatedly during a continuing condition, such as a long-running scan or a persistent misconfiguration. If analysts have to open and close ten alerts that all describe the same root event, the system is wasting human attention. A better design groups related alerts by common pivots, such as identity, host, or service, and represents them as a single case with multiple supporting signals. Grouping reduces cognitive load because the analyst investigates one story rather than ten fragments. It also improves escalation decisions because the combined picture often reveals severity more accurately than separate alerts do. When you tune for duplication, you are not weakening detection, you are improving how the detection results are packaged as work. Over time, grouping and de-duplication can be the difference between a manageable queue and a constant flood.

Threshold tuning is another area where feedback loops matter, because thresholds are rarely correct on the first attempt and they can drift as the environment changes. A threshold might be a number of events in a window, a deviation from baseline, or a rule that triggers when activity crosses a certain level. If a threshold is too low, you produce constant noise, and if it is too high, you miss early warning, which is why thresholds must be treated as adjustable controls, not fixed truths. Feedback loops help because you can observe how often a threshold fires, how often those alerts are true, and whether the threshold captures the meaningful cases you care about. A common improvement is to use different thresholds for different contexts, such as stricter thresholds for privileged identities and more tolerant thresholds for noisy systems, because a single global threshold often fails. Another improvement is to add a second condition that increases confidence, such as requiring both an unusual count and an unusual target. When thresholds evolve based on outcomes, they become aligned with real behavior rather than with assumptions.

Sometimes the cause of noise is not the detection logic but the data, and feedback loops should help you recognize that difference so you do not tune in the wrong direction. If alerts are frequently inconclusive because key fields are missing, the right fix might be to improve parsing, normalization, or enrichment rather than to suppress the detection. If timestamps are inconsistent, correlations can look suspicious when they are actually normal, so improving time consistency may reduce false positives more than changing the rule. If asset criticality tags are wrong, detections might escalate normal activity on low-impact systems, creating artificial urgency, so fixing asset mapping can reduce noise immediately. If identity mappings are stale, you might misjudge role-based expectations and classify normal admin work as anomalous, which is again an enrichment problem. The tuning lesson is that noise is a signal about the whole monitoring system, not just about the rule that fired. Good feedback loops allow you to decide whether the correct adjustment is to the rule, the context, the data pipeline, or the enrichment sources. When you treat tuning as system improvement, you avoid the trap of hiding problems by disabling detections.

A related tuning best practice is to separate what should be an alert from what should be an investigation aid, because not every interesting event should page a human. Many events are valuable during investigations, such as detailed system logs or routine state changes, but they are too common to serve as primary alerts. When these events are promoted to alerts, they create backlog and alert fatigue, even though the information itself is useful. A better approach is to reserve alerts for patterns that justify immediate attention, while ensuring that supporting telemetry is easy to pivot to when an alert occurs. This distinction reduces noise without reducing investigative capability, and it also helps detection designers stay disciplined about what the alert implies. If an alert cannot justify a clear next action, it may be better as context rather than as a trigger. This is why tuning often involves demoting noisy rules into supporting queries and promoting better behavior-based rules into alerts. When teams make this shift intentionally, the queue becomes more meaningful, and analysts feel less trapped by constant low-value work.

Feedback loops also benefit from intentional review cadence, because tuning becomes chaotic when changes are made randomly and without measurement. If tuning is done impulsively, one person might suppress an alert type after a frustrating day, while another person later wonders why coverage disappeared. A healthier approach is to review alert outcomes regularly, identify the top contributors to backlog, and select a small number of changes that are likely to deliver the biggest workload reduction. After changes are made, you observe the effect, such as whether alert volume decreased, whether true positive rate improved, and whether time to triage fell. This is the loop in action: observe, adjust, measure, and repeat. Regular cadence matters because it prevents tuning from being either constant thrashing or long-term neglect, and it creates a shared understanding of what is being changed and why. It also protects against blind suppression because the goal is not to reduce alerts at any cost, but to reduce avoidable work while preserving meaningful detection. When tuning is measured and intentional, backlog shrinks in a controlled way rather than in a risky way.

As backlog begins to shrink, a mature program does not stop tuning, because environments change and attackers adapt, and the alert system must keep learning to stay useful. New applications appear, business processes change, and user behavior shifts, which can turn previously accurate thresholds into noise or can hide new risky patterns under a new normal. This is why feedback loops should include awareness of change events in the organization, such as migrations, new authentication methods, or changes in access policies, because those shifts can explain sudden changes in alert behavior. A good tuning culture asks whether a spike in alerts represents real risk or merely a change in normal operations, and it adjusts the detection logic accordingly. At the same time, tuning should avoid making rules so forgiving that they normalize dangerous behavior, which is a risk when repeated suspicious patterns are explained away as the new normal without evidence. The balance is to update baselines and context with real operational knowledge while keeping the use case goal intact. Continuous tuning is less about chasing perfection and more about maintaining alignment between detections and reality.

Another part of sustainable tuning is making it easy for analysts to contribute feedback without turning them into rule engineers, because analysts are often the best source of real-world outcome knowledge. Analysts see which alerts waste time, which alerts are missing context, and which patterns repeat, and their observations can be captured as structured feedback that detection engineers can act on. When that communication is weak, tuning slows down and frustration grows, and the S O C becomes stuck with the same noise patterns. A strong program encourages analysts to note what made an alert false, what extra context would have helped, and what related signals would have increased confidence, because those are actionable inputs. Over time, this creates a shared language between detection design and alert handling, and that shared language is what makes the feedback loop efficient. It also prevents a common cultural failure where tuning is seen as someone else’s problem, leaving analysts to suffer through noisy queues without improvement. When analysts and engineers collaborate through structured feedback, backlog reduction becomes a predictable outcome rather than a hope.

As we close, keep the big picture clear: tuning noisy detections is not about silencing the system, it is about using feedback loops to replace repeated low-value work with fewer, higher-confidence signals that people can act on quickly. Noise is any alert output that consumes attention without producing proportional value, and backlog is the operational consequence that turns monitoring into a slow, risky process. Feedback loops make tuning effective because they use real outcomes to guide changes, such as improving specificity with context, shifting from single events to behavior sequences, reducing duplicates through grouping, and adjusting thresholds based on evidence. The same loop helps you recognize when the true problem is data quality or enrichment, so you fix the system rather than hiding symptoms by suppressing alerts. A sustainable tuning practice uses regular review cadence, measured change, and collaboration between analysts and detection designers so improvements compound over time. When tuning is treated as an operational improvement program, not a one-time cleanup, alert queues become manageable, triage becomes faster, and true positives become easier to see. That is how the S O C earns trust and stays effective as both the environment and the threat landscape evolve.